An application of interactive fuzzy optimization model for redesigning supply chain for resilience

Kanokporn Kungwalsong; Mendoza, Abraham; Vasanth Kamath; Subramanian Pazhani; Marmolejo Saucedo, José Antonio

doi:10.1007/s10479-022-04542-5

An application of interactive fuzzy optimization model for redesigning supply chain for resilience

Journal

Annals of Operations Research

ISSN

0254-5330

1572-9338

Date Issued

2022

Author(s)

Kanokporn Kungwalsong

Mendoza, Abraham

Facultad de Ingeniería - CampGDL

Vasanth Kamath

Subramanian Pazhani

Marmolejo Saucedo, José Antonio

Facultad de Ingeniería - CampCM

Type

text::journal::journal article

DOI

10.1007/s10479-022-04542-5

URL

https://scripta.up.edu.mx/handle/20.500.12552/3960

Abstract

AbstractSupply chain disruptions compel professionals all over the world to consider alternate strategies for addressing these issues and remaining profitable in the future. In this study, we considered a four-stage global supply chain and designed the network with the objectives of maximizing profit and minimizing disruption risk. We quantified and modeled disruption risk as a function of the geographic diversification of facilities called supply density (evaluated based on the interstage distance between nodes) to mitigate the risk caused by disruptions. Furthermore, we developed a bi-criteria mixed-integer linear programming model for designing the supply chain in order to maximize profit and supply density. We propose an interactive fuzzy optimization algorithm that generates efficient frontiers by systematically taking decision-maker inputs and solves the bi-criteria model problem in the context of a realistic example. We also conducted disruption analysis using a discrete set of disruption scenarios to determine the advantages of the network design from the bi-criteria model over the traditional profit maximization model. Our study demonstrates that the network design from the bi-criteria model has a 2% higher expected profit and a 2.2% lower profit variance under disruption than the traditional profit maximization solution. We envisage that this model will help firms evaluate the trade-offs between mitigation benefits and mitigation costs.